Cited by 32Bioinformatics and Genomic NetworksFungal and yeast genetics researchGenomics and Phylogenetic Studies

Broad functional profiling of fission yeast proteins using phenomics and machine learning

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Rodríguez-López, María, Bordin, Nicola, Lees, Jon, Scholes, Harry, Hassan, Shaimaa, Saintain, Quentin, Kamrad, Stephan, Orengo, Christine +1 more·2023·eLife

Published version of a preprint

Abstract

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

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Authors
Rodríguez-López, M., Bordin, N., Lees, J., Scholes, H., Hassan, S., Saintain, Q., Kamrad, S., Orengo, C., & Bähler, J.
Affiliations
  • University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment
  • Institute of Structural and Molecular Biology (GB)
  • University College London, Institute of Structural and Molecular Biology
  • University College London (GB)
Venue
eLife
Published
2023-10-03
Volume
12
ISSN
2050-084X (ISSN-L)
OpenAlex
W4387311335
Cited by
18
References
100
Citations by year
2026: 5 · 2025: 7 · 2024: 6
FWCI
1.59
Type
journal article
Language
en
Open access
Locations
  • eLife (publishedVersion, cc-by)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
  • Explore Bristol Research (submittedVersion, cc-by)
Funders
  • Biotechnology and Biological Sciences Research Council (BB/R009597/1)
  • Newton-Moshara Fund (Scholarship)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
protein function prediction, Schizosaccharomyces pombe, deletion mutants, non-essential genes, oxidative stress resistance, guilt by association, gene ontology annotation, protein networks, protein homology, cellular aging, phenomics

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